Data Intake
| Index | stub | file | data_type | taxon_string | translation_table |
|---|---|---|---|---|---|
| 0 | KX808498-truncated | KX808498-truncated.gb | GenBank | Caulerpa_cliftonii_HV03798 | 11 |
| 1 | KY509313-truncated | KY509313-truncated.gb | GenBank | Avrainvillea_mazei_HV02664 | 11 |
| 2 | MH591083-truncated | MH591083-truncated.gb | GenBank | Flabellia_petiolata_HV01202 | 11 |
| 3 | MH591084-truncated | MH591084-truncated.gb | GenBank | Flabellia_petiolata_HV01202 | 11 |
| 4 | MH591085-truncated | MH591085-truncated.gb | GenBank | Flabellia_petiolata_HV01202 | 11 |
| 5 | NC_026795-truncated | NC_026795-truncated.txt | GenBank | Bryopsis_plumosa_WEST4718 | 11 |
| 6 | KY819064-truncated-cds | KY819064-truncated.cds.fasta | CDS | Chlorodesmis_fastigiata_HV03865 | 11 |
| 7 | KX808497-truncated | KX808497-truncated.fa | CDS | Derbesia_sp_WEST4838 | 11 |
Orthofinder
| Name | Value |
|---|---|
| Number of species | 8 |
| Number of genes | 65 |
| Number of genes in orthogroups | 41 |
| Number of unassigned genes | 24 |
| Percentage of genes in orthogroups | 63.1 |
| Percentage of unassigned genes | 36.9 |
| Number of orthogroups | 7 |
| Number of species-specific orthogroups | 0 |
| Number of genes in species-specific orthogroups | 0 |
| Percentage of genes in species-specific orthogroups | 0.0 |
| Mean orthogroup size | 5.9 |
| Median orthogroup size | 6.0 |
| G50 (assigned genes) | 6 |
| G50 (all genes) | 6 |
| O50 (assigned genes) | 4 |
| O50 (all genes) | 6 |
| Number of orthogroups with all species present | 0 |
| Number of single-copy orthogroups | 0 |
| Date | 2024-05-20 |
| Orthogroups file | Orthogroups.tsv |
| Unassigned genes file | Orthogroups_UnassignedGenes.tsv |
| Per-species statistics | Statistics_PerSpecies.tsv |
| Overall statistics | Statistics_Overall.tsv |
| Orthogroups shared between species | Orthogroups_SpeciesOverlaps.tsv |
Average number of genes per-species in orthogroup
| None | Average number of genes per-species in orthogroup | Number of orthogroups | Percentage of orthogroups | Number of genes | Percentage of genes |
|---|---|---|---|---|---|
| 0 | <1 | 7 | 100.0 | 41 | 100.0 |
| 1 | '1 | 0 | 0.0 | 0 | 0.0 |
| 2 | '2 | 0 | 0.0 | 0 | 0.0 |
| 3 | '3 | 0 | 0.0 | 0 | 0.0 |
| 4 | '4 | 0 | 0.0 | 0 | 0.0 |
| 5 | '5 | 0 | 0.0 | 0 | 0.0 |
| 6 | '6 | 0 | 0.0 | 0 | 0.0 |
| 7 | '7 | 0 | 0.0 | 0 | 0.0 |
| 8 | '8 | 0 | 0.0 | 0 | 0.0 |
| 9 | '9 | 0 | 0.0 | 0 | 0.0 |
| 10 | '10 | 0 | 0.0 | 0 | 0.0 |
| 11 | 11-15 | 0 | 0.0 | 0 | 0.0 |
| 12 | 16-20 | 0 | 0.0 | 0 | 0.0 |
| 13 | 21-50 | 0 | 0.0 | 0 | 0.0 |
| 14 | 51-100 | 0 | 0.0 | 0 | 0.0 |
| 15 | 101-150 | 0 | 0.0 | 0 | 0.0 |
| 16 | 151-200 | 0 | 0.0 | 0 | 0.0 |
| 17 | 201-500 | 0 | 0.0 | 0 | 0.0 |
| 18 | 501-1000 | 0 | 0.0 | 0 | 0.0 |
| 19 | '1001+ | 0 | 0.0 | 0 | 0.0 |
Number of Orthogroups vs. Number of OGs
| None | Number of genes | Number of genes in orthogroups | Number of unassigned genes | Percentage of genes in orthogroups | Percentage of unassigned genes | Number of orthogroups containing species | Percentage of orthogroups containing species | Number of species-specific orthogroups | Number of genes in species-specific orthogroups | Percentage of genes in species-specific orthogroups |
|---|---|---|---|---|---|---|---|---|---|---|
| KX808497-truncated.translated | 7.0 | 7.0 | 0.0 | 100.0 | 0.0 | 7.0 | 100.0 | 0.0 | 0.0 | 0.0 |
| KX808498-truncated.translated | 31.0 | 8.0 | 23.0 | 25.8 | 74.2 | 7.0 | 100.0 | 0.0 | 0.0 | 0.0 |
| KY509313-truncated.translated | 7.0 | 7.0 | 0.0 | 100.0 | 0.0 | 7.0 | 100.0 | 0.0 | 0.0 | 0.0 |
| KY819064-truncated-cds.translated | 7.0 | 7.0 | 0.0 | 100.0 | 0.0 | 7.0 | 100.0 | 0.0 | 0.0 | 0.0 |
| MH591083-truncated.translated | 3.0 | 3.0 | 0.0 | 100.0 | 0.0 | 3.0 | 42.9 | 0.0 | 0.0 | 0.0 |
| MH591084-truncated.translated | 2.0 | 2.0 | 0.0 | 100.0 | 0.0 | 2.0 | 28.6 | 0.0 | 0.0 | 0.0 |
| MH591085-truncated.translated | 1.0 | 0.0 | 1.0 | 0.0 | 100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| NC_026795-truncated.translated | 7.0 | 7.0 | 0.0 | 100.0 | 0.0 | 7.0 | 100.0 | 0.0 | 0.0 | 0.0 |
results/orthofinder/output/Orthogroup_Sequences/OG0000000.fa results/orthofinder/output/Orthogroup_Sequences/OG0000001.fa results/orthofinder/output/Orthogroup_Sequences/OG0000002.fa results/orthofinder/output/Orthogroup_Sequences/OG0000003.fa results/orthofinder/output/Orthogroup_Sequences/OG0000004.fa results/orthofinder/output/Orthogroup_Sequences/OG0000006.fa
results/orthofinder/orthosnap/OG0000005/OG0000005_orthosnap_0.fa
Alignment
results/alignment/trimmed_protein/OG0000000.trimmed.protein.alignment.fa results/alignment/trimmed_protein/OG0000004.trimmed.protein.alignment.fa
Supermatrix
General Characteristics ======================= 6 Number of taxa 796 Alignment length 100 Parsimony informative sites 100 Variable sites 685 Constant sites Character Frequencies ===================== Y 176 W 52 V 338 T 242 S 200 R 217 Q 230 P 268 N 222 M 77 L 412 K 301 I 309 H 97 G 410 F 230 E 271 D 231 C 79 A 366 - 48
IQ-TREE 2.2.0.3 COVID-edition built Aug 2 2022
Input file name: results/supermatrix/supermatrix.protein.fa
Type of analysis: ModelFinder + tree reconstruction + ultrafast bootstrap (1000 replicates)
Random seed number: 897197
REFERENCES
----------
To cite IQ-TREE please use:
Bui Quang Minh, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf,
Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear (2020)
IQ-TREE 2: New models and efficient methods for phylogenetic inference
in the genomic era. Mol. Biol. Evol., in press.
https://doi.org/10.1093/molbev/msaa015
To cite ModelFinder please use:
Subha Kalyaanamoorthy, Bui Quang Minh, Thomas KF Wong, Arndt von Haeseler,
and Lars S Jermiin (2017) ModelFinder: Fast model selection for
accurate phylogenetic estimates. Nature Methods, 14:587–589.
https://doi.org/10.1038/nmeth.4285
Since you used ultrafast bootstrap (UFBoot) please also cite:
Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh,
and Le Sy Vinh (2018) UFBoot2: Improving the ultrafast bootstrap
approximation. Mol. Biol. Evol., 35:518–522.
https://doi.org/10.1093/molbev/msx281
SEQUENCE ALIGNMENT
------------------
Input data: 6 sequences with 796 amino-acid sites
Number of constant sites: 553 (= 69.4724% of all sites)
Number of invariant (constant or ambiguous constant) sites: 553 (= 69.4724% of all sites)
Number of parsimony informative sites: 100
Number of distinct site patterns: 255
ModelFinder
-----------
Best-fit model according to BIC: cpREV+I+G4
List of models sorted by BIC scores:
Model LogL AIC w-AIC AICc w-AICc BIC w-BIC
cpREV+I+G4 -4754.045 9530.090 + 0.864 9530.427 + 0.904 9581.566 + 0.563
cpREV+G4 -4757.639 9535.278 + 0.0645 9535.558 + 0.0695 9582.074 + 0.437
LG+I+G4 -4762.923 9547.846 - 0.00012 9548.183 - 0.000126 9599.322 - 7.85e-05
LG+G4 -4769.787 9559.575 - 3.42e-07 9559.855 - 3.68e-07 9606.371 - 2.31e-06
Q.pfam+I+G4 -4772.958 9567.915 - 5.28e-09 9568.252 - 5.53e-09 9619.391 - 3.44e-09
Q.pfam+G4 -4779.840 9579.680 - 1.47e-11 9579.960 - 1.59e-11 9626.476 - 9.96e-11
WAG+I+G4 -4778.199 9578.397 - 2.8e-11 9578.734 - 2.93e-11 9629.873 - 1.82e-11
WAG+G4 -4782.001 9584.002 - 1.7e-12 9584.283 - 1.83e-12 9630.798 - 1.15e-11
VT+G4 -4783.028 9586.055 - 6.08e-13 9586.335 - 6.54e-13 9632.851 - 4.11e-12
VT+I+G4 -4779.908 9581.816 - 5.06e-12 9582.153 - 5.3e-12 9633.291 - 3.3e-12
LG+I -4785.119 9590.237 - 7.51e-14 9590.517 - 8.08e-14 9637.033 - 5.08e-13
Q.plant+I+G4 -4784.005 9590.009 - 8.41e-14 9590.346 - 8.81e-14 9641.485 - 5.49e-14
Q.yeast+I+G4 -4784.243 9590.486 - 6.63e-14 9590.823 - 6.94e-14 9641.962 - 4.32e-14
rtREV+I+G4 -4785.917 9593.835 - 1.24e-14 9594.171 - 1.3e-14 9645.310 - 8.1e-15
rtREV+G4 -4791.371 9602.742 - 1.45e-16 9603.022 - 1.56e-16 9649.538 - 9.79e-16
JTTDCMut+I+G4 -4789.487 9600.973 - 3.5e-16 9601.310 - 3.66e-16 9652.449 - 2.28e-16
Q.yeast+G4 -4793.400 9606.800 - 1.9e-17 9607.080 - 2.05e-17 9653.596 - 1.29e-16
Q.plant+G4 -4794.205 9608.410 - 8.5e-18 9608.691 - 9.15e-18 9655.206 - 5.75e-17
JTT+I+G4 -4791.124 9604.249 - 6.81e-17 9604.585 - 7.12e-17 9655.724 - 4.44e-17
JTTDCMut+G4 -4795.768 9611.536 - 1.78e-18 9611.816 - 1.92e-18 9658.332 - 1.2e-17
JTT+G4 -4797.364 9614.729 - 3.61e-19 9615.009 - 3.88e-19 9661.525 - 2.44e-18
Q.insect+I+G4 -4795.237 9612.475 - 1.11e-18 9612.811 - 1.17e-18 9663.950 - 7.26e-19
Blosum62+G4 -4799.131 9618.261 - 6.17e-20 9618.542 - 6.64e-20 9665.057 - 4.17e-19
Blosum62+I+G4 -4796.305 9614.610 - 3.83e-19 9614.947 - 4.01e-19 9666.086 - 2.5e-19
PMB+G4 -4804.054 9628.108 - 4.49e-22 9628.388 - 4.83e-22 9674.904 - 3.04e-21
Q.insect+G4 -4804.135 9628.271 - 4.14e-22 9628.551 - 4.45e-22 9675.067 - 2.8e-21
cpREV+F+I+G4 -4737.617 9535.233 + 0.066 9537.665 - 0.0242 9675.621 - 2.12e-21
cpREV+F+G4 -4741.076 9540.152 - 0.00564 9542.424 - 0.00224 9675.861 - 1.88e-21
PMB+I+G4 -4801.637 9625.275 - 1.85e-21 9625.612 - 1.94e-21 9676.750 - 1.21e-21
mtART+F+I+G4 -4744.357 9548.714 - 7.8e-05 9551.145 - 2.87e-05 9689.102 - 2.51e-24
mtZOA+F+I+G4 -4745.935 9551.870 - 1.61e-05 9554.301 - 5.91e-06 9692.258 - 5.18e-25
LG+F+I+G4 -4746.272 9552.543 - 1.15e-05 9554.975 - 4.22e-06 9692.931 - 3.7e-25
Dayhoff+I+G4 -4810.213 9642.426 - 3.49e-25 9642.763 - 3.65e-25 9693.902 - 2.28e-25
DCMut+I+G4 -4810.333 9642.665 - 3.1e-25 9643.002 - 3.24e-25 9694.141 - 2.02e-25
Dayhoff+G4 -4815.703 9651.406 - 3.92e-27 9651.687 - 4.22e-27 9698.202 - 2.65e-26
DCMut+G4 -4815.774 9651.549 - 3.65e-27 9651.829 - 3.93e-27 9698.345 - 2.47e-26
mtART+F+G4 -4752.459 9562.918 - 6.43e-08 9565.189 - 2.56e-08 9698.626 - 2.14e-26
LG+F+G4 -4752.616 9563.233 - 5.49e-08 9565.505 - 2.18e-08 9698.941 - 1.83e-26
mtZOA+F+G4 -4754.209 9566.418 - 1.12e-08 9568.689 - 4.44e-09 9702.126 - 3.73e-27
Q.pfam+F+I+G4 -4752.279 9564.558 - 2.83e-08 9566.989 - 1.04e-08 9704.946 - 9.1e-28
mtInv+F+I+G4 -4753.769 9567.537 - 6.38e-09 9569.969 - 2.34e-09 9707.925 - 2.05e-28
WAG+F+I+G4 -4753.892 9567.784 - 5.64e-09 9570.216 - 2.07e-09 9708.172 - 1.81e-28
WAG+F+G4 -4757.613 9573.227 - 3.71e-10 9575.498 - 1.48e-10 9708.935 - 1.24e-28
Q.pfam+F+G4 -4758.639 9575.278 - 1.33e-10 9577.550 - 5.29e-11 9710.987 - 4.44e-29
rtREV+F+I+G4 -4755.824 9571.649 - 8.16e-10 9574.080 - 3e-10 9712.037 - 2.62e-29
mtInv+F+G4 -4760.247 9578.495 - 2.66e-11 9580.766 - 1.06e-11 9714.203 - 8.89e-30
rtREV+F+G4 -4761.112 9580.224 - 1.12e-11 9582.496 - 4.46e-12 9715.933 - 3.74e-30
Q.yeast+F+I+G4 -4760.383 9580.766 - 8.55e-12 9583.198 - 3.14e-12 9721.154 - 2.75e-31
Q.plant+F+I+G4 -4762.682 9585.365 - 8.58e-13 9587.796 - 3.15e-13 9725.752 - 2.76e-32
mtREV+F+I+G4 -4763.806 9587.612 - 2.79e-13 9590.043 - 1.02e-13 9728.000 - 8.97e-33
Q.yeast+F+G4 -4768.290 9594.579 - 8.56e-15 9596.851 - 3.41e-15 9730.288 - 2.86e-33
mtREV+F+G4 -4770.353 9598.705 - 1.09e-15 9600.977 - 4.33e-16 9734.414 - 3.63e-34
VT+F+G4 -4771.629 9601.259 - 3.04e-16 9603.530 - 1.21e-16 9736.967 - 1.01e-34
Q.plant+F+G4 -4771.916 9601.831 - 2.28e-16 9604.103 - 9.07e-17 9737.540 - 7.61e-35
VT+F+I+G4 -4768.882 9597.764 - 1.74e-15 9600.196 - 6.4e-16 9738.152 - 5.6e-35
Q.insect+F+I+G4 -4768.958 9597.915 - 1.62e-15 9600.347 - 5.93e-16 9738.303 - 5.19e-35
Dayhoff+F+I+G4 -4769.817 9599.634 - 6.84e-16 9602.066 - 2.51e-16 9740.022 - 2.2e-35
DCMut+F+I+G4 -4769.947 9599.894 - 6.01e-16 9602.325 - 2.21e-16 9740.282 - 1.93e-35
JTTDCMut+F+I+G4 -4771.470 9602.940 - 1.31e-16 9605.371 - 4.81e-17 9743.328 - 4.21e-36
Dayhoff+F+G4 -4775.697 9609.395 - 5.19e-18 9611.666 - 2.07e-18 9745.103 - 1.73e-36
DCMut+F+G4 -4775.776 9609.552 - 4.8e-18 9611.823 - 1.91e-18 9745.260 - 1.6e-36
JTT+F+I+G4 -4773.051 9606.102 - 2.69e-17 9608.534 - 9.9e-18 9746.490 - 8.66e-37
Q.insect+F+G4 -4776.671 9611.342 - 1.96e-18 9613.614 - 7.8e-19 9747.050 - 6.55e-37
JTTDCMut+F+G4 -4777.486 9612.972 - 8.68e-19 9615.244 - 3.45e-19 9748.681 - 2.9e-37
HIVb+I+G4 -4838.442 9698.885 - 1.92e-37 9699.221 - 2.01e-37 9750.360 - 1.25e-37
PMB+F+G4 -4778.450 9614.900 - 3.31e-19 9617.172 - 1.32e-19 9750.609 - 1.11e-37
mtMet+F+I+G4 -4775.135 9610.270 - 3.35e-18 9612.702 - 1.23e-18 9750.658 - 1.08e-37
JTT+F+G4 -4778.998 9615.995 - 1.92e-19 9618.267 - 7.62e-20 9751.704 - 6.39e-38
PMB+F+I+G4 -4776.222 9612.445 - 1.13e-18 9614.876 - 4.15e-19 9752.833 - 3.63e-38
mtMet+F+G4 -4783.430 9624.859 - 2.28e-21 9627.131 - 9.06e-22 9760.568 - 7.6e-40
HIVb+G4 -4847.221 9714.442 - 8.03e-41 9714.722 - 8.65e-41 9761.238 - 5.44e-40
Blosum62+F+G4 -4784.806 9627.612 - 5.75e-22 9629.884 - 2.29e-22 9763.321 - 1.92e-40
Blosum62+F+I+G4 -4782.259 9624.519 - 2.7e-21 9626.950 - 9.92e-22 9764.907 - 8.68e-41
FLU+I+G4 -4845.984 9713.969 - 1.02e-40 9714.305 - 1.07e-40 9765.444 - 6.64e-41
Q.mammal+I+G4 -4850.226 9722.451 - 1.46e-42 9722.788 - 1.53e-42 9773.927 - 9.55e-43
Q.bird+I+G4 -4851.171 9724.342 - 5.69e-43 9724.679 - 5.95e-43 9775.818 - 3.71e-43
FLU+G4 -4856.287 9732.574 - 9.28e-45 9732.854 - 9.99e-45 9779.370 - 6.28e-44
Q.mammal+G4 -4860.506 9741.012 - 1.36e-46 9741.293 - 1.47e-46 9787.808 - 9.24e-46
Q.bird+G4 -4863.081 9746.163 - 1.04e-47 9746.443 - 1.12e-47 9792.959 - 7.03e-47
FLU+F+I+G4 -4803.431 9666.863 - 1.72e-30 9669.294 - 6.33e-31 9807.251 - 5.54e-50
mtMAM+F+I+G4 -4805.644 9671.288 - 1.89e-31 9673.719 - 6.93e-32 9811.676 - 6.07e-51
HIVb+F+I+G4 -4806.965 9673.931 - 5.03e-32 9676.362 - 1.85e-32 9814.319 - 1.62e-51
mtVer+F+I+G4 -4808.228 9676.457 - 1.42e-32 9678.888 - 5.23e-33 9816.845 - 4.58e-52
Q.mammal+F+I+G4 -4808.873 9677.746 - 7.47e-33 9680.178 - 2.74e-33 9818.134 - 2.4e-52
FLU+F+G4 -4812.951 9683.903 - 3.44e-34 9686.174 - 1.37e-34 9819.611 - 1.15e-52
HIVb+F+G4 -4815.656 9689.312 - 2.3e-35 9691.583 - 9.15e-36 9825.020 - 7.68e-54
mtMAM+F+G4 -4816.650 9691.300 - 8.51e-36 9693.572 - 3.39e-36 9827.009 - 2.84e-54
Q.mammal+F+G4 -4818.621 9695.242 - 1.19e-36 9697.514 - 4.72e-37 9830.951 - 3.96e-55
mtVer+F+G4 -4821.298 9700.595 - 8.16e-38 9702.867 - 3.25e-38 9836.303 - 2.72e-56
Q.bird+F+I+G4 -4818.887 9697.773 - 3.35e-37 9700.205 - 1.23e-37 9838.161 - 1.08e-56
Q.bird+F+G4 -4830.313 9718.627 - 9.91e-42 9720.898 - 3.94e-42 9854.335 - 3.31e-60
FLAVI+I+G4 -4909.651 9841.303 - 2.28e-68 9841.639 - 2.38e-68 9892.778 - 1.48e-68
FLAVI+F+I+G4 -4846.739 9753.479 - 2.68e-49 9755.910 - 9.84e-50 9893.867 - 8.62e-69
mtZOA+I+G4 -4912.946 9847.893 - 8.44e-70 9848.230 - 8.83e-70 9899.369 - 5.5e-70
mtZOA+G4 -4920.530 9861.060 - 1.17e-72 9861.341 - 1.26e-72 9907.856 - 7.9e-72
FLAVI+G4 -4921.129 9862.258 - 6.41e-73 9862.538 - 6.9e-73 9909.054 - 4.34e-72
FLAVI+F+G4 -4859.578 9777.156 - 1.93e-54 9779.428 - 7.69e-55 9912.865 - 6.46e-73
mtART+I+G4 -4936.533 9895.066 - 4.82e-80 9895.402 - 5.04e-80 9946.541 - 3.14e-80
mtART+G4 -4943.775 9907.550 - 9.38e-83 9907.830 - 1.01e-82 9954.346 - 6.35e-82
LG -4967.424 9952.849 - 1.37e-92 9953.078 - 1.51e-92 9994.965 - 9.59e-91
HIVw+I+G4 -4961.731 9945.463 - 5.49e-91 9945.799 - 5.74e-91 9996.938 - 3.58e-91
mtREV+I+G4 -4965.210 9952.420 - 1.69e-92 9952.756 - 1.77e-92 10003.895 - 1.1e-92
mtREV+G4 -4973.869 9967.739 - 7.98e-96 9968.019 - 8.59e-96 10014.535 - 5.4e-95
HIVw+F+I+G4 -4907.718 9875.435 - 8.82e-76 9877.867 - 3.24e-76 10015.823 - 2.84e-95
mtMet+I+G4 -4971.840 9965.680 - 2.23e-95 9966.017 - 2.34e-95 10017.156 - 1.46e-95
HIVw+G4 -4977.561 9975.122 - 1.99e-97 9975.402 - 2.14e-97 10021.918 - 1.35e-96
mtMet+G4 -4981.233 9982.467 - 5.06e-99 9982.747 - 5.45e-99 10029.263 - 3.42e-98
HIVw+F+G4 -4922.673 9903.345 - 7.67e-82 9905.617 - 3.05e-82 10039.054 - 2.56e-100
mtMAM+I+G4 -5003.477 10028.953 - 4.07e-109 10029.290 - 4.26e-109 10080.429 - 2.65e-109
mtInv+I+G4 -5006.654 10035.307 - 1.7e-110 10035.644 - 1.78e-110 10086.783 - 1.11e-110
mtMAM+G4 -5012.493 10044.986 - 1.34e-112 10045.266 - 1.45e-112 10091.782 - 9.09e-112
mtVer+I+G4 -5009.382 10040.765 - 1.11e-111 10041.102 - 1.16e-111 10092.240 - 7.23e-112
mtInv+G4 -5015.079 10050.159 - 1.01e-113 10050.439 - 1.09e-113 10096.955 - 6.84e-113
mtVer+G4 -5020.644 10061.287 - 3.88e-116 10061.567 - 4.17e-116 10108.083 - 2.62e-115
AIC, w-AIC : Akaike information criterion scores and weights.
AICc, w-AICc : Corrected AIC scores and weights.
BIC, w-BIC : Bayesian information criterion scores and weights.
Plus signs denote the 95% confidence sets.
Minus signs denote significant exclusion.
SUBSTITUTION PROCESS
--------------------
Model of substitution: cpREV+I+G4
State frequencies: (model)
Model of rate heterogeneity: Invar+Gamma with 4 categories
Proportion of invariable sites: 0.4626
Gamma shape alpha: 0.7873
Category Relative_rate Proportion
0 0 0.4626
1 0.1728 0.1344
2 0.7480 0.1344
3 1.7740 0.1344
4 4.7484 0.1344
Relative rates are computed as MEAN of the portion of the Gamma distribution falling in the category.
MAXIMUM LIKELIHOOD TREE
-----------------------
Log-likelihood of the tree: -4753.6149 (s.e. 129.2064)
Unconstrained log-likelihood (without tree): -3410.0651
Number of free parameters (#branches + #model parameters): 11
Akaike information criterion (AIC) score: 9529.2298
Corrected Akaike information criterion (AICc) score: 9529.5665
Bayesian information criterion (BIC) score: 9580.7054
Total tree length (sum of branch lengths): 0.8705
Sum of internal branch lengths: 0.2131 (24.4767% of tree length)
NOTE: Tree is UNROOTED although outgroup taxon 'Avrainvillea_mazei_HV02664' is drawn at root
Numbers in parentheses are ultrafast bootstrap support (%)
+---------------------------------------------Avrainvillea_mazei_HV02664
|
| +----------------------Bryopsis_plumosa_WEST4718
+-----------------| (98)
| +-------------------------------------Derbesia_sp_WEST4838
|
| +---------------------------------Caulerpa_cliftonii_HV03798
+------------------------| (100)
| +---------------------Chlorodesmis_fastigiata_HV03865
+----------| (89)
+-----Flabellia_petiolata_HV01202
Tree in newick format:
(Avrainvillea_mazei_HV02664:0.1774013445,(Bryopsis_plumosa_WEST4718:0.0911758714,Derbesia_sp_WEST4838:0.1476274396)98:0.0699129242,(Caulerpa_cliftonii_HV03798:0.1301706564,(Chlorodesmis_fastigiata_HV03865:0.0865181849,Flabellia_petiolata_HV01202:0.0245108158)89:0.0442680079)100:0.0988801322);
CONSENSUS TREE
--------------
Consensus tree is constructed from 1000 bootstrap trees
Log-likelihood of consensus tree: -4753.614908
Robinson-Foulds distance between ML tree and consensus tree: 0
Branches with support >0.000000% are kept (extended consensus)
Branch lengths are optimized by maximum likelihood on original alignment
Numbers in parentheses are bootstrap supports (%)
+---------------------------------------------Avrainvillea_mazei_HV02664
|
| +----------------------Bryopsis_plumosa_WEST4718
+-----------------| (98)
| +-------------------------------------Derbesia_sp_WEST4838
|
| +---------------------------------Caulerpa_cliftonii_HV03798
+------------------------| (100)
| +---------------------Chlorodesmis_fastigiata_HV03865
+----------| (89)
+-----Flabellia_petiolata_HV01202
Consensus tree in newick format:
(Avrainvillea_mazei_HV02664:0.1774144319,(Bryopsis_plumosa_WEST4718:0.0911749918,Derbesia_sp_WEST4838:0.1476344774)98:0.0699199306,(Caulerpa_cliftonii_HV03798:0.1302019886,(Chlorodesmis_fastigiata_HV03865:0.0865481698,Flabellia_petiolata_HV01202:0.0244952462)89:0.0442549918)100:0.0988875453);
TIME STAMP
----------
Date and time: Mon May 20 05:39:59 2024
Total CPU time used: 2.735088 seconds (0h:0m:2s)
Total wall-clock time used: 2.767166841 seconds (0h:0m:2s)
IQ-TREE multicore version 2.2.0.3 COVID-edition for Linux 64-bit built Aug 2 2022
Developed by Bui Quang Minh, James Barbetti, Nguyen Lam Tung,
Olga Chernomor, Heiko Schmidt, Dominik Schrempf, Michael Woodhams, Ly Trong Nhan.
Host: mrc-hvlab (AVX2, FMA3, 251 GB RAM)
Command: iqtree2 -s results/supermatrix/supermatrix.protein.fa -bb 1000 -m TEST -nt 1 -redo -pre results/supermatrix/supermatrix.protein
Seed: 897197 (Using SPRNG - Scalable Parallel Random Number Generator)
Time: Mon May 20 05:39:53 2024
Kernel: AVX+FMA - 1 threads (64 CPU cores detected)
HINT: Use -nt option to specify number of threads because your CPU has 64 cores!
HINT: -nt AUTO will automatically determine the best number of threads to use.
Reading alignment file results/supermatrix/supermatrix.protein.fa ... Fasta format detected
Reading fasta file: done in 0.000153274 secs using 49.58% CPU
Alignment most likely contains protein sequences
Constructing alignment: done in 0.00035199 secs using 50% CPU
Alignment has 6 sequences with 796 columns, 255 distinct patterns
100 parsimony-informative, 143 singleton sites, 553 constant sites
Gap/Ambiguity Composition p-value
Analyzing sequences: done in 7.52509e-06 secs using 39.87% CPU
1 Avrainvillea_mazei_HV02664 1.13% passed 99.95%
2 Bryopsis_plumosa_WEST4718 0.38% passed 100.00%
3 Caulerpa_cliftonii_HV03798 0.00% passed 100.00%
4 Chlorodesmis_fastigiata_HV03865 1.51% passed 100.00%
5 Derbesia_sp_WEST4838 0.63% passed 100.00%
6 Flabellia_petiolata_HV01202 2.39% passed 100.00%
**** TOTAL 1.01% 0 sequences failed composition chi2 test (p-value<5%; df=19)
Checking for duplicate sequences: done in 1.65384e-05 secs using 48.37% CPU
Create initial parsimony tree by phylogenetic likelihood library (PLL)... 0.000 seconds
Perform fast likelihood tree search using LG+I+G model...
Estimate model parameters (epsilon = 5.000)
Perform nearest neighbor interchange...
Optimizing NNI: done in 0.00738169 secs using 99.9% CPU
Estimate model parameters (epsilon = 1.000)
1. Initial log-likelihood: -4763.107
Optimal log-likelihood: -4763.040
Proportion of invariable sites: 0.357
Gamma shape alpha: 0.456
Parameters optimization took 1 rounds (0.008 sec)
Time for fast ML tree search: 0.042 seconds
NOTE: ModelFinder requires 2 MB RAM!
ModelFinder will test up to 224 protein models (sample size: 796) ...
No. Model -LnL df AIC AICc BIC
1 LG 4967.424 9 9952.849 9953.078 9994.965
2 LG+I 4785.119 10 9590.237 9590.517 9637.033
3 LG+G4 4769.787 10 9559.575 9559.855 9606.371
4 LG+I+G4 4762.923 11 9547.846 9548.183 9599.322
7 LG+F+G4 4752.616 29 9563.233 9565.505 9698.941
8 LG+F+I+G4 4746.272 30 9552.543 9554.975 9692.931
11 WAG+G4 4782.001 10 9584.002 9584.283 9630.798
12 WAG+I+G4 4778.199 11 9578.397 9578.734 9629.873
15 WAG+F+G4 4757.613 29 9573.227 9575.498 9708.935
16 WAG+F+I+G4 4753.892 30 9567.784 9570.216 9708.172
19 JTT+G4 4797.364 10 9614.729 9615.009 9661.525
20 JTT+I+G4 4791.124 11 9604.249 9604.585 9655.724
23 JTT+F+G4 4778.998 29 9615.995 9618.267 9751.704
24 JTT+F+I+G4 4773.051 30 9606.102 9608.534 9746.490
WARNING: Normalizing state frequencies so that sum of them equals to 1
27 Q.pfam+G4 4779.840 10 9579.680 9579.960 9626.476
WARNING: Normalizing state frequencies so that sum of them equals to 1
28 Q.pfam+I+G4 4772.958 11 9567.915 9568.252 9619.391
WARNING: Normalizing state frequencies so that sum of them equals to 1
31 Q.pfam+F+G4 4758.639 29 9575.278 9577.550 9710.987
WARNING: Normalizing state frequencies so that sum of them equals to 1
32 Q.pfam+F+I+G4 4752.279 30 9564.558 9566.989 9704.946
WARNING: Normalizing state frequencies so that sum of them equals to 1
35 Q.bird+G4 4863.081 10 9746.163 9746.443 9792.959
WARNING: Normalizing state frequencies so that sum of them equals to 1
36 Q.bird+I+G4 4851.171 11 9724.342 9724.679 9775.818
WARNING: Normalizing state frequencies so that sum of them equals to 1
39 Q.bird+F+G4 4830.313 29 9718.627 9720.898 9854.335
WARNING: Normalizing state frequencies so that sum of them equals to 1
40 Q.bird+F+I+G4 4818.887 30 9697.773 9700.205 9838.161
WARNING: Normalizing state frequencies so that sum of them equals to 1
43 Q.mammal+G4 4860.506 10 9741.012 9741.293 9787.808
WARNING: Normalizing state frequencies so that sum of them equals to 1
44 Q.mammal+I+G4 4850.226 11 9722.451 9722.788 9773.927
WARNING: Normalizing state frequencies so that sum of them equals to 1
47 Q.mammal+F+G4 4818.621 29 9695.242 9697.514 9830.951
WARNING: Normalizing state frequencies so that sum of them equals to 1
48 Q.mammal+F+I+G4 4808.873 30 9677.746 9680.178 9818.134
51 Q.insect+G4 4804.135 10 9628.271 9628.551 9675.067
52 Q.insect+I+G4 4795.237 11 9612.475 9612.811 9663.950
55 Q.insect+F+G4 4776.671 29 9611.342 9613.614 9747.050
56 Q.insect+F+I+G4 4768.958 30 9597.915 9600.347 9738.303
WARNING: Normalizing state frequencies so that sum of them equals to 1
59 Q.plant+G4 4794.205 10 9608.410 9608.691 9655.206
WARNING: Normalizing state frequencies so that sum of them equals to 1
60 Q.plant+I+G4 4784.005 11 9590.009 9590.346 9641.485
WARNING: Normalizing state frequencies so that sum of them equals to 1
63 Q.plant+F+G4 4771.916 29 9601.831 9604.103 9737.540
WARNING: Normalizing state frequencies so that sum of them equals to 1
64 Q.plant+F+I+G4 4762.682 30 9585.365 9587.796 9725.752
WARNING: Normalizing state frequencies so that sum of them equals to 1
67 Q.yeast+G4 4793.400 10 9606.800 9607.080 9653.596
WARNING: Normalizing state frequencies so that sum of them equals to 1
68 Q.yeast+I+G4 4784.243 11 9590.486 9590.823 9641.962
WARNING: Normalizing state frequencies so that sum of them equals to 1
71 Q.yeast+F+G4 4768.290 29 9594.579 9596.851 9730.288
WARNING: Normalizing state frequencies so that sum of them equals to 1
72 Q.yeast+F+I+G4 4760.383 30 9580.766 9583.198 9721.154
75 JTTDCMut+G4 4795.768 10 9611.536 9611.816 9658.332
76 JTTDCMut+I+G4 4789.487 11 9600.973 9601.310 9652.449
79 JTTDCMut+F+G4 4777.486 29 9612.972 9615.244 9748.681
80 JTTDCMut+F+I+G4 4771.470 30 9602.940 9605.371 9743.328
83 DCMut+G4 4815.774 10 9651.549 9651.829 9698.345
84 DCMut+I+G4 4810.333 11 9642.665 9643.002 9694.141
87 DCMut+F+G4 4775.776 29 9609.552 9611.823 9745.260
88 DCMut+F+I+G4 4769.947 30 9599.894 9602.325 9740.282
91 VT+G4 4783.028 10 9586.055 9586.335 9632.851
92 VT+I+G4 4779.908 11 9581.816 9582.153 9633.291
95 VT+F+G4 4771.629 29 9601.259 9603.530 9736.967
96 VT+F+I+G4 4768.882 30 9597.764 9600.196 9738.152
99 PMB+G4 4804.054 10 9628.108 9628.388 9674.904
100 PMB+I+G4 4801.637 11 9625.275 9625.612 9676.750
103 PMB+F+G4 4778.450 29 9614.900 9617.172 9750.609
104 PMB+F+I+G4 4776.222 30 9612.445 9614.876 9752.833
107 Blosum62+G4 4799.131 10 9618.261 9618.542 9665.057
108 Blosum62+I+G4 4796.305 11 9614.610 9614.947 9666.086
111 Blosum62+F+G4 4784.806 29 9627.612 9629.884 9763.321
112 Blosum62+F+I+G4 4782.259 30 9624.519 9626.950 9764.907
115 Dayhoff+G4 4815.703 10 9651.406 9651.687 9698.202
116 Dayhoff+I+G4 4810.213 11 9642.426 9642.763 9693.902
119 Dayhoff+F+G4 4775.697 29 9609.395 9611.666 9745.103
120 Dayhoff+F+I+G4 4769.817 30 9599.634 9602.066 9740.022
123 mtREV+G4 4973.869 10 9967.739 9968.019 10014.535
124 mtREV+I+G4 4965.210 11 9952.420 9952.756 10003.895
127 mtREV+F+G4 4770.353 29 9598.705 9600.977 9734.414
128 mtREV+F+I+G4 4763.806 30 9587.612 9590.043 9728.000
131 mtART+G4 4943.775 10 9907.550 9907.830 9954.346
132 mtART+I+G4 4936.533 11 9895.066 9895.402 9946.541
135 mtART+F+G4 4752.459 29 9562.918 9565.189 9698.626
136 mtART+F+I+G4 4744.357 30 9548.714 9551.145 9689.102
139 mtZOA+G4 4920.530 10 9861.060 9861.341 9907.856
140 mtZOA+I+G4 4912.946 11 9847.893 9848.230 9899.369
143 mtZOA+F+G4 4754.209 29 9566.418 9568.689 9702.126
144 mtZOA+F+I+G4 4745.935 30 9551.870 9554.301 9692.258
147 mtMet+G4 4981.233 10 9982.467 9982.747 10029.263
148 mtMet+I+G4 4971.840 11 9965.680 9966.017 10017.156
151 mtMet+F+G4 4783.430 29 9624.859 9627.131 9760.568
152 mtMet+F+I+G4 4775.135 30 9610.270 9612.702 9750.658
155 mtVer+G4 5020.644 10 10061.287 10061.567 10108.083
156 mtVer+I+G4 5009.382 11 10040.765 10041.102 10092.240
159 mtVer+F+G4 4821.298 29 9700.595 9702.867 9836.303
160 mtVer+F+I+G4 4808.228 30 9676.457 9678.888 9816.845
163 mtInv+G4 5015.079 10 10050.159 10050.439 10096.955
164 mtInv+I+G4 5006.654 11 10035.307 10035.644 10086.783
167 mtInv+F+G4 4760.247 29 9578.495 9580.766 9714.203
168 mtInv+F+I+G4 4753.769 30 9567.537 9569.969 9707.925
171 mtMAM+G4 5012.493 10 10044.986 10045.266 10091.782
172 mtMAM+I+G4 5003.477 11 10028.953 10029.290 10080.429
175 mtMAM+F+G4 4816.650 29 9691.300 9693.572 9827.009
176 mtMAM+F+I+G4 4805.644 30 9671.288 9673.719 9811.676
WARNING: Normalizing state frequencies so that sum of them equals to 1
179 FLAVI+G4 4921.129 10 9862.258 9862.538 9909.054
WARNING: Normalizing state frequencies so that sum of them equals to 1
180 FLAVI+I+G4 4909.651 11 9841.303 9841.639 9892.778
WARNING: Normalizing state frequencies so that sum of them equals to 1
183 FLAVI+F+G4 4859.578 29 9777.156 9779.428 9912.865
WARNING: Normalizing state frequencies so that sum of them equals to 1
184 FLAVI+F+I+G4 4846.739 30 9753.479 9755.910 9893.867
187 HIVb+G4 4847.221 10 9714.442 9714.722 9761.238
188 HIVb+I+G4 4838.442 11 9698.885 9699.221 9750.360
191 HIVb+F+G4 4815.656 29 9689.312 9691.583 9825.020
192 HIVb+F+I+G4 4806.965 30 9673.931 9676.362 9814.319
195 HIVw+G4 4977.561 10 9975.122 9975.402 10021.918
196 HIVw+I+G4 4961.731 11 9945.463 9945.799 9996.938
199 HIVw+F+G4 4922.673 29 9903.345 9905.617 10039.054
200 HIVw+F+I+G4 4907.718 30 9875.435 9877.867 10015.823
203 FLU+G4 4856.287 10 9732.574 9732.854 9779.370
204 FLU+I+G4 4845.984 11 9713.969 9714.305 9765.444
207 FLU+F+G4 4812.951 29 9683.903 9686.174 9819.611
208 FLU+F+I+G4 4803.431 30 9666.863 9669.294 9807.251
211 rtREV+G4 4791.371 10 9602.742 9603.022 9649.538
212 rtREV+I+G4 4785.917 11 9593.835 9594.171 9645.310
215 rtREV+F+G4 4761.112 29 9580.224 9582.496 9715.933
216 rtREV+F+I+G4 4755.824 30 9571.649 9574.080 9712.037
219 cpREV+G4 4757.639 10 9535.278 9535.558 9582.074
220 cpREV+I+G4 4754.045 11 9530.090 9530.427 9581.566
223 cpREV+F+G4 4741.076 29 9540.152 9542.424 9675.861
224 cpREV+F+I+G4 4737.617 30 9535.233 9537.665 9675.621
Akaike Information Criterion: cpREV+I+G4
Corrected Akaike Information Criterion: cpREV+I+G4
Bayesian Information Criterion: cpREV+I+G4
Best-fit model: cpREV+I+G4 chosen according to BIC
All model information printed to results/supermatrix/supermatrix.protein.model.gz
CPU time for ModelFinder: 2.753 seconds (0h:0m:2s)
Wall-clock time for ModelFinder: 2.773 seconds (0h:0m:2s)
Generating 1000 samples for ultrafast bootstrap (seed: 897197)...
NOTE: 2 MB RAM (0 GB) is required!
Estimate model parameters (epsilon = 0.100)
Thoroughly optimizing +I+G parameters from 10 start values...
Init pinv, alpha: 0.000, 0.547 / Estimate: 0.000, 0.237 / LogL: -4757.639
Init pinv, alpha: 0.077, 0.547 / Estimate: 0.080, 0.273 / LogL: -4756.785
Init pinv, alpha: 0.154, 0.547 / Estimate: 0.158, 0.320 / LogL: -4755.996
Init pinv, alpha: 0.232, 0.547 / Estimate: 0.236, 0.381 / LogL: -4755.220
Init pinv, alpha: 0.309, 0.547 / Estimate: 0.314, 0.465 / LogL: -4754.490
Init pinv, alpha: 0.386, 0.547 / Estimate: 0.390, 0.589 / LogL: -4753.890
Init pinv, alpha: 0.463, 0.547 / Estimate: 0.463, 0.787 / LogL: -4753.615
Init pinv, alpha: 0.540, 0.547 / Estimate: 0.524, 1.098 / LogL: -4753.966
Init pinv, alpha: 0.618, 0.547 / Estimate: 0.531, 1.143 / LogL: -4754.058
Init pinv, alpha: 0.695, 0.547 / Estimate: 0.528, 1.124 / LogL: -4754.018
Optimal pinv,alpha: 0.463, 0.787 / LogL: -4753.615
Parameters optimization took 0.479 sec
Wrote distance file to...
Computing ML distances based on estimated model parameters...
Calculating distance matrix: done in 0.00468276 secs using 99.88% CPU
Computing ML distances took 0.004751 sec (of wall-clock time) 0.004749 sec (of CPU time)
Setting up auxiliary I and S matrices: done in 5.10458e-05 secs
Constructing RapidNJ tree: done in 3.30219e-05 secs
Computing RapidNJ tree took 0.000207 sec (of wall-clock time) 0.000000 sec (of CPU time)
Log-likelihood of RapidNJ tree: -4753.615
--------------------------------------------------------------------
| INITIALIZING CANDIDATE TREE SET |
--------------------------------------------------------------------
Generating 99 parsimony trees... 0.023 second
Computing log-likelihood of 6 initial trees ... 0.014 seconds
Current best score: -4753.615
Do NNI search on 7 best initial trees
Optimizing NNI: done in 0.00767932 secs using 99.96% CPU
Optimizing NNI: done in 0.0169954 secs using 99.99% CPU
Optimizing NNI: done in 0.0169308 secs using 99.99% CPU
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Optimizing NNI: done in 0.0171724 secs using 99.98% CPU
Optimizing NNI: done in 0.0173175 secs using 99.99% CPU
Finish initializing candidate tree set (7)
Current best tree score: -4753.615 / CPU time: 0.155
Number of iterations: 7
--------------------------------------------------------------------
| OPTIMIZING CANDIDATE TREE SET |
--------------------------------------------------------------------
Optimizing NNI: done in 0.0170258 secs using 99.99% CPU
Optimizing NNI: done in 0.0167453 secs using 99.99% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.0265731 secs using 99.99% CPU
Iteration 10 / LogL: -4753.632 / Time: 0h:0m:0s
Optimizing NNI: done in 0.0166733 secs using 99.99% CPU
Optimizing NNI: done in 0.016746 secs using 99.99% CPU
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Optimizing NNI: done in 0.0267519 secs using 100% CPU
Iteration 20 / LogL: -4753.691 / Time: 0h:0m:0s
Optimizing NNI: done in 0.0272953 secs using 99.8% CPU
Optimizing NNI: done in 0.0167081 secs using 99.99% CPU
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Iteration 30 / LogL: -4753.632 / Time: 0h:0m:1s (0h:0m:2s left)
Optimizing NNI: done in 0.0266316 secs using 100% CPU
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Optimizing NNI: done in 0.0265018 secs using 99.99% CPU
Optimizing NNI: done in 0.016564 secs using 99.99% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.016547 secs using 99.98% CPU
UPDATE BEST LOG-LIKELIHOOD: -4753.615
Optimizing NNI: done in 0.0168966 secs using 99.99% CPU
Optimizing NNI: done in 0.0165192 secs using 99.99% CPU
Iteration 40 / LogL: -4753.615 / Time: 0h:0m:1s (0h:0m:2s left)
Optimizing NNI: done in 0.0169498 secs using 99.99% CPU
Optimizing NNI: done in 0.0167769 secs using 99.99% CPU
Optimizing NNI: done in 0.0166699 secs using 76.13% CPU
Optimizing NNI: done in 0.00734444 secs using 99.98% CPU
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Optimizing NNI: done in 0.0162681 secs using 99.99% CPU
Optimizing NNI: done in 0.0168751 secs using 99.99% CPU
Iteration 50 / LogL: -4753.627 / Time: 0h:0m:1s (0h:0m:1s left)
Log-likelihood cutoff on original alignment: -4786.654
Optimizing NNI: done in 0.0165264 secs using 99.99% CPU
Optimizing NNI: done in 0.0171308 secs using 99.99% CPU
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Iteration 60 / LogL: -4753.632 / Time: 0h:0m:1s (0h:0m:1s left)
Optimizing NNI: done in 0.0164515 secs using 99.91% CPU
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Optimizing NNI: done in 0.0169598 secs using 100% CPU
Optimizing NNI: done in 0.0169314 secs using 100% CPU
Iteration 70 / LogL: -4753.628 / Time: 0h:0m:2s (0h:0m:0s left)
Optimizing NNI: done in 0.0268798 secs using 99.99% CPU
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Optimizing NNI: done in 0.00739516 secs using 99.98% CPU
Iteration 80 / LogL: -4761.550 / Time: 0h:0m:2s (0h:0m:0s left)
Optimizing NNI: done in 0.0266108 secs using 99.99% CPU
Optimizing NNI: done in 0.016728 secs using 99.99% CPU
Optimizing NNI: done in 0.0171669 secs using 93.34% CPU
Optimizing NNI: done in 0.0170218 secs using 92.89% CPU
Optimizing NNI: done in 0.0265598 secs using 100% CPU
Optimizing NNI: done in 0.00736945 secs using 99.98% CPU
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Optimizing NNI: done in 0.0163807 secs using 99.99% CPU
Iteration 90 / LogL: -4753.615 / Time: 0h:0m:2s (0h:0m:0s left)
Optimizing NNI: done in 0.0168165 secs using 99.99% CPU
Optimizing NNI: done in 0.0169384 secs using 100% CPU
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Optimizing NNI: done in 0.0169688 secs using 99.99% CPU
Optimizing NNI: done in 0.0267198 secs using 100% CPU
Optimizing NNI: done in 0.0163229 secs using 99.99% CPU
Optimizing NNI: done in 0.00732773 secs using 99.98% CPU
Optimizing NNI: done in 0.0167877 secs using 100% CPU
Optimizing NNI: done in 0.0073535 secs using 99.98% CPU
Optimizing NNI: done in 0.016791 secs using 99.99% CPU
Iteration 100 / LogL: -4753.673 / Time: 0h:0m:2s (0h:0m:0s left)
Log-likelihood cutoff on original alignment: -4786.654
NOTE: Bootstrap correlation coefficient of split occurrence frequencies: 1.000
Optimizing NNI: done in 0.0171537 secs using 99.9% CPU
TREE SEARCH COMPLETED AFTER 101 ITERATIONS / Time: 0h:0m:2s
--------------------------------------------------------------------
| FINALIZING TREE SEARCH |
--------------------------------------------------------------------
Performs final model parameters optimization
Estimate model parameters (epsilon = 0.010)
1. Initial log-likelihood: -4753.615
Optimal log-likelihood: -4753.615
Proportion of invariable sites: 0.463
Gamma shape alpha: 0.787
Parameters optimization took 1 rounds (0.008 sec)
BEST SCORE FOUND : -4753.615
Creating bootstrap support values...
Split supports printed to NEXUS file results/supermatrix/supermatrix.protein.splits.nex
Total tree length: 0.870
Total number of iterations: 101
CPU time used for tree search: 2.186 sec (0h:0m:2s)
Wall-clock time used for tree search: 2.203 sec (0h:0m:2s)
Total CPU time used: 2.735 sec (0h:0m:2s)
Total wall-clock time used: 2.752 sec (0h:0m:2s)
Computing bootstrap consensus tree...
Reading input file results/supermatrix/supermatrix.protein.splits.nex...
6 taxa and 13 splits.
Consensus tree written to results/supermatrix/supermatrix.protein.contree
Reading input trees file results/supermatrix/supermatrix.protein.contree
Log-likelihood of consensus tree: -4753.615
Analysis results written to:
IQ-TREE report: results/supermatrix/supermatrix.protein.iqtree
Maximum-likelihood tree: results/supermatrix/supermatrix.protein.treefile
Likelihood distances: results/supermatrix/supermatrix.protein.mldist
Ultrafast bootstrap approximation results written to:
Split support values: results/supermatrix/supermatrix.protein.splits.nex
Consensus tree: results/supermatrix/supermatrix.protein.contree
Screen log file: results/supermatrix/supermatrix.protein.log
ALISIM COMMAND
--------------
--alisim simulated_MSA -t results/supermatrix/supermatrix.protein.treefile -m "cpREV+I{0.462594}+G4{0.787278}" --length 796
Date and Time: Mon May 20 05:39:59 2024
Gene Tree
Supertree
(Derbesia_sp_WEST4838,(Bryopsis_plumosa_WEST4718,(Avrainvillea_mazei_HV02664,((Chlorodesmis_fastigiata_HV03865,Flabellia_petiolata_HV01202)0.87:0.6931471805599453,Caulerpa_cliftonii_HV03798)0.87:0.6931471805599453)0.87:0.6931471805599453):0.0);
Workflow
Bibliography
- 1
- Deren A. R. Eaton. Toytree: A minimalist tree visualization and manipulation library for Python. Methods in Ecology and Evolution, 11:187–191, 2020. doi:10.1111/2041-210X.13313.
- 2
- David M. Emms and Steven Kelly. Orthofinder: phylogenetic orthology inference for comparative genomics. Genome Biology, 20(1):238, 2019. URL: https://doi.org/10.1186/s13059-019-1832-y, doi:10.1186/s13059-019-1832-y.
- 3
- Diep Thi Hoang, Olga Chernomor, Arndt von Haeseler, Bui Quang Minh, and Le Sy Vinh. UFBoot2: Improving the Ultrafast Bootstrap Approximation. Molecular Biology and Evolution, 35(2):518–522, 10 2017. URL: https://doi.org/10.1093/molbev/msx281, arXiv:https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf, doi:10.1093/molbev/msx281.
- 4
- Subha Kalyaanamoorthy, Bui Quang Minh, Thomas K F Wong, Arndt von Haeseler, and Lars S Jermiin. Modelfinder: fast model selection for accurate phylogenetic estimates. Nature Methods, 14(6):587–589, 2017. URL: https://doi.org/10.1038/nmeth.4285, doi:10.1038/nmeth.4285.
- 5
- Kazutaka Katoh and Daron M. Standley. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Molecular Biology and Evolution, 30(4):772–780, 01 2013. URL: https://doi.org/10.1093/molbev/mst010, arXiv:https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf, doi:10.1093/molbev/mst010.
- 6
- Bui Quang Minh, Heiko A Schmidt, Olga Chernomor, Dominik Schrempf, Michael D Woodhams, Arndt von Haeseler, and Robert Lanfear. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Molecular Biology and Evolution, 37(5):1530–1534, 02 2020. URL: https://doi.org/10.1093/molbev/msaa015, arXiv:https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf, doi:10.1093/molbev/msaa015.
- 7
- Jacob L Steenwyk, III Buida, Thomas J, Abigail L Labella, Yuanning Li, Xing-Xing Shen, and Antonis Rokas. PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data. Bioinformatics, 37(16):2325–2331, 02 2021. URL: https://doi.org/10.1093/bioinformatics/btab096, arXiv:https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf, doi:10.1093/bioinformatics/btab096.
- 8
- Jacob L. Steenwyk, Thomas J. Buida, Carla Gonçalves, Dayna C. Goltz, Grace Morales, Matthew E. Mead, Abigail L. LaBella, Christina M. Chavez, Jonathan E. Schmitz, Maria Hadjifrangiskou, Yuanning Li, and Antonis Rokas. BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data. biorxiv, oct 2021. URL: https://doi.org/10.1101%2F2021.10.02.462868, doi:10.1101/2021.10.02.462868.
- 9
- Jacob L. Steenwyk, Thomas J. Buida, III, Yuanning Li, Xing-Xing Shen, and Antonis Rokas. Clipkit: a multiple sequence alignment trimming software for accurate phylogenomic inference. PLOS Biology, 18(12):1–17, 12 2020. URL: https://doi.org/10.1371/journal.pbio.3001007, doi:10.1371/journal.pbio.3001007.
- 10
- Chao Zhang, Maryam Rabiee, Erfan Sayyari, and Siavash Mirarab. Astral-iii: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics, 19(6):153, 2018. URL: https://doi.org/10.1186/s12859-018-2129-y, doi:10.1186/s12859-018-2129-y.
@article{10.1093/molbev/mst010,
author = "Katoh, Kazutaka and Standley, Daron M.",
title = "{MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability}",
journal = "Molecular Biology and Evolution",
volume = "30",
number = "4",
pages = "772-780",
year = "2013",
month = "01",
abstract = "{We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.}",
issn = "0737-4038",
doi = "10.1093/molbev/mst010",
url = "https://doi.org/10.1093/molbev/mst010",
eprint = "https://academic.oup.com/mbe/article-pdf/30/4/772/6420419/mst010.pdf"
}
@article{Emms2019,
author = "Emms, David M. and Kelly, Steven",
type = "Journal Article",
title = "OrthoFinder: phylogenetic orthology inference for comparative genomics",
journal = "Genome Biology",
number = "1",
doi = "10.1186/s13059-019-1832-y",
volume = "20",
pages = "238",
url = "https://doi.org/10.1186/s13059-019-1832-y",
year = "2019",
abstract = "Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.",
isbn = "1474-760X",
DA = "2019/11/14"
}
@article{10.1371/journal.pbio.3001007,
author = "Steenwyk, Jacob L. and Buida, III, Thomas J. and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
doi = "10.1371/journal.pbio.3001007",
journal = "PLOS Biology",
publisher = "Public Library of Science",
title = "ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference",
year = "2020",
month = "12",
volume = "18",
url = "https://doi.org/10.1371/journal.pbio.3001007",
pages = "1-17",
abstract = "Highly divergent sites in multiple sequence alignments (MSAs), which can stem from erroneous inference of homology and saturation of substitutions, are thought to negatively impact phylogenetic inference. Thus, several different trimming strategies have been developed for identifying and removing these sites prior to phylogenetic inference. However, a recent study reported that doing so can worsen inference, underscoring the need for alternative alignment trimming strategies. Here, we introduce ClipKIT, an alignment trimming software that, rather than identifying and removing putatively phylogenetically uninformative sites, instead aims to identify and retain parsimony-informative sites, which are known to be phylogenetically informative. To test the efficacy of ClipKIT, we examined the accuracy and support of phylogenies inferred from 14 different alignment trimming strategies, including those implemented in ClipKIT, across nearly 140,000 alignments from a broad sampling of evolutionary histories. Phylogenies inferred from ClipKIT-trimmed alignments are accurate, robust, and time saving. Furthermore, ClipKIT consistently outperformed other trimming methods across diverse datasets, suggesting that strategies based on identifying and retaining parsimony-informative sites provide a robust framework for alignment trimming.",
number = "12"
}
@article{Steenwyk_2021,
author = "Steenwyk, Jacob L. and Buida, Thomas J. and Gon{\c{c}}alves, Carla and Goltz, Dayna C. and Morales, Grace and Mead, Matthew E. and LaBella, Abigail L. and Chavez, Christina M. and Schmitz, Jonathan E. and Hadjifrangiskou, Maria and Li, Yuanning and Rokas, Antonis",
doi = "10.1101/2021.10.02.462868",
url = "https://doi.org/10.1101\%2F2021.10.02.462868",
year = "2021",
month = "oct",
journal = "biorxiv",
publisher = "Cold Spring Harbor Laboratory",
title = "{BioKIT}: a versatile toolkit for processing and analyzing diverse types of sequence data"
}
@article{10.1093/molbev/msx281,
author = "Hoang, Diep Thi and Chernomor, Olga and von Haeseler, Arndt and Minh, Bui Quang and Vinh, Le Sy",
title = "{UFBoot2: Improving the Ultrafast Bootstrap Approximation}",
journal = "Molecular Biology and Evolution",
volume = "35",
number = "2",
pages = "518-522",
year = "2017",
month = "10",
abstract = "{The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap approaches. Here, we present UFBoot2, which substantially accelerates UFBoot and reduces the risk of overestimating branch supports due to polytomies or severe model violations. Additionally, UFBoot2 provides suitable bootstrap resampling strategies for phylogenomic data. UFBoot2 is 778 times (median) faster than SBS and 8.4 times (median) faster than RAxML rapid bootstrap on tested data sets. UFBoot2 is implemented in the IQ-TREE software package version 1.6 and freely available at http://www.iqtree.org.}",
issn = "0737-4038",
doi = "10.1093/molbev/msx281",
url = "https://doi.org/10.1093/molbev/msx281",
eprint = "https://academic.oup.com/mbe/article-pdf/35/2/518/24367824/msx281.pdf"
}
@article{10.1093/bioinformatics/btab096,
author = "Steenwyk, Jacob L and Buida, Thomas J, III and Labella, Abigail L and Li, Yuanning and Shen, Xing-Xing and Rokas, Antonis",
title = "{PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data}",
journal = "Bioinformatics",
volume = "37",
number = "16",
pages = "2325-2331",
year = "2021",
month = "02",
abstract = "{Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock and collapsing bipartitions (internal branches) with low support.To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene–gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining and deriving biological meaning from increasingly large phylogenomic datasets.PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/) and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT).Supplementary data are available at Bioinformatics online.}",
issn = "1367-4803",
doi = "10.1093/bioinformatics/btab096",
url = "https://doi.org/10.1093/bioinformatics/btab096",
eprint = "https://academic.oup.com/bioinformatics/article-pdf/37/16/2325/39948152/btab096.pdf"
}
@article{eaton_toytree_2020,
author = "Eaton, Deren A. R.",
title = "Toytree: {A} minimalist tree visualization and manipulation library for {Python}",
volume = "11",
doi = "10.1111/2041-210X.13313",
journal = "Methods in Ecology and Evolution",
year = "2020",
pages = "187--191"
}
@article{10.1093/molbev/msaa015,
author = "Minh, Bui Quang and Schmidt, Heiko A and Chernomor, Olga and Schrempf, Dominik and Woodhams, Michael D and von Haeseler, Arndt and Lanfear, Robert",
title = "{IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era}",
journal = "Molecular Biology and Evolution",
volume = "37",
number = "5",
pages = "1530-1534",
year = "2020",
month = "02",
abstract = "{IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.}",
issn = "0737-4038",
doi = "10.1093/molbev/msaa015",
url = "https://doi.org/10.1093/molbev/msaa015",
eprint = "https://academic.oup.com/mbe/article-pdf/37/5/1530/33386032/msaa015.pdf"
}
@article{Kalyaanamoorthy2017,
author = "Kalyaanamoorthy, Subha and Minh, Bui Quang and Wong, Thomas K F and von Haeseler, Arndt and Jermiin, Lars S",
type = "Journal Article",
title = "ModelFinder: fast model selection for accurate phylogenetic estimates",
journal = "Nature Methods",
number = "6",
doi = "10.1038/nmeth.4285",
volume = "14",
pages = "587--589",
url = "https://doi.org/10.1038/nmeth.4285",
year = "2017",
abstract = "ModelFinder is a fast model-selection method that greatly improves the accuracy of phylogenetic estimates.",
issn = "1548-7105",
DA = "2017/06/01"
}
@article{Zhang2018,
author = "Zhang, Chao and Rabiee, Maryam and Sayyari, Erfan and Mirarab, Siavash",
type = "Journal Article",
title = "ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees",
journal = "BMC Bioinformatics",
number = "6",
doi = "10.1186/s12859-018-2129-y",
volume = "19",
pages = "153",
url = "https://doi.org/10.1186/s12859-018-2129-y",
year = "2018",
abstract = "Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.",
issn = "1471-2105",
DA = "2018/05/08"
}All taxa are found in orthogroups.
File(s) not Fasta or Genbank file. Suffix from file 'NC_026795-truncated.txt' is not Fasta or Genbank. File is assumed to be in Fasta format.